Paper Citation Record · LEDGER

Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2403.02502.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2403.02502 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:22:46.685290Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 45c31990-362d-4cfc-a117-e735ae0d5ab2 · inbound

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models cites this paper.

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 137

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:20:59.285843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b60306bd-6d8a-499b-a9cb-8c15f2fe02c3 · inbound

LLM Agents Are the Antidote to Walled Gardens cites this paper.

LLM Agents Are the Antidote to Walled Gardens Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:44:29.334526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5f4bc58d-d935-4fc3-a9bc-4aeb5ac374f0 · inbound

Morae: Proactively Pausing UI Agents for User Choices cites this paper.

Morae: Proactively Pausing UI Agents for User Choices Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-05T14:22:46.685290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12da300c-43b1-44f2-98bc-522a405c6a9b · inbound

C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving cites this paper.

C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-13T23:28:26.147204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b8590005-2a87-4ab2-9c57-800e77e5cf8d · inbound

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling cites this paper.

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:21:00.765636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0cfeddd9-b98e-4306-a053-a6e73a32ab6b · inbound

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling cites this paper.

Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:01:18.099969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d3140644-00be-4e5f-8aca-39e20086c8f1 · inbound

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents cites this paper.

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:46:10.341474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3c960811-bc23-4655-8161-f73b79095dc4 · inbound

SkillEvolver: Skill Learning as a Meta-Skill cites this paper.

SkillEvolver: Skill Learning as a Meta-Skill Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:46:30.933210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d1c0b134-c71e-4b73-90ff-762b459da0c9 · inbound

Test-Time Deep Thinking to Explore Implicit Rules cites this paper.

Test-Time Deep Thinking to Explore Implicit Rules Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:54:38.217679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c280ebd1-32be-4256-a36c-38317269654f · inbound

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments cites this paper.

Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-06-29T16:53:40.485548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 465c0c7f-110c-41a7-8dd9-56c9b0b2ffa3 · inbound

Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents cites this paper.

Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:23:30.930994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0255e5ca-c9ab-478c-997a-1620b4704b82 · inbound

Agent System Operations: Categorization, Challenges, and Future Directions cites this paper.

Agent System Operations: Categorization, Challenges, and Future Directions Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:16:24.960638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation df35d8ce-acec-4f5e-9f04-c505f631a1b5 · inbound

HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning cites this paper.

HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:47:41.660750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e8c4f43d-36dc-4651-a696-660148e50156 · inbound

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application cites this paper.

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 285

Resolution: verified exact; arxiv_id, observed 2026-06-27T09:50:48.540036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4b8f4369-4b4a-45bf-a075-4a9fb3033cb8 · inbound

OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation cites this paper.

OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 115

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T20:48:56.469629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a7b3a7f0-21cc-44b3-bd62-fe69125b1956 · inbound

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents cites this paper.

Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T06:59:38.168063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b3c80fb3-719f-4334-8921-b1545b440c14 · inbound

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents cites this paper.

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 72

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T08:39:42.730106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9e11918f-c40e-4fcf-9e2c-4f56c9e8a377 · inbound

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories cites this paper.

Agentic-DPO: From Imitation to Agentic Policy Optimization on Expert Trajectories Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-14T10:33:54.851493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4d25527-dbe6-4bf0-8e64-2cf2c4db6b9d · inbound

Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems cites this paper.

Leveraging Trajectory Graphs for Pre-Execution Error Diagnosis in Agentic LLM Systems Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-31T00:46:11.219569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.